Data mining can be used to collect and/or analyze data. Data mining can include, for example, the process of extracting patterns from data and/or transforming data into information. Data mining can be used in a number of applications, such as, for example, marketing, surveillance, fraud detection, and scientific discovery, among other applications.
One type of data mining is spatial (e.g., geographical) data mining. Geographical data mining can be used to find patterns in and/or identify objects in geography. For instance, geographical data mining can be used to find patterns in and/or identify objects contained in a geographical image or map, such as, for example, geographical objects, topological features, and spatial relationships contained in a geographical image or map, among other objects.
Previous geographical data mining approaches may use simple representations of objects contained in a geographical image or map to find patterns in and/or identify the objects. That is, previous geographical data mining approaches may be able to find patterns in and/or identify simple objects contained in a geographical image or map.
However, previous geographical data mining approaches may not use complex representations of objects contained in a geographical image or map to find patterns in and/or identify the objects. That is, previous geographical data mining approaches may not be able to find patterns in and/or identify complex objects contained in a geographical image or map. For example, previous geographical data mining approaches may not be able to identify complex geographical objects (e.g., geographical objects having lines and/or polylines) or complex spatial relationships (e.g., relationships having non-Euclidian distances, direction, connectivity, and/or interaction through attributed geographic space) contained in a geographical image or map.